Bayesian classification and non-Bayesian label estimation via EM algorithm to identify differentially expressed genes: a comparative study.
Gene classification problem is studied considering the ratio of gene expression levels, X, in two-channel microarrays and a non-observed categorical variable indicating how differentially expressed the gene is: non differentially expressed, down-regulated or up-regulated. Supposing X from a mixture of Gamma distributions, two methods are proposed and results are compared. The first method is based on an hierarchical Bayesian model. The conditional predictive probability of a gene to belong to each group is calculated and the gene is assigned to the group for which this conditional probability is higher. The second method uses EM algorithm to estimate the most likely group label for each gene, that is, to assign the gene to the group which contains it with the higher estimated probability.
Antunes
Marília
M
University of Lisbon, Faculty of Sciences and Center of Statistics and Applications, DEIO, C6, Piso 4, 1749-016 Lisboa, Portugal. marilia.antunes@fc.ul.pt
Sousa
Lisete
L
D000465:Algorithms; D001499:Bayes Theorem; D001699:Biometry / Q000379:methods*; D015536:Down-Regulation; D020869:Gene Expression Profiling / Q000706:statistics & numerical data*; D006801:Humans; D015233:Models, Statistical; D020411:Oligonucleotide Array Sequence Analysis / Q000706:statistics & numerical data; D015854:Up-Regulation